Fine-grained cross-view localization seeks to predict ground-level camera positions within GPS-tagged aerial images by matching ground and aerial views. Existing methods often rely on large-scale ground truth annotations from specific regions, but performance degrades due to domain shifts when models trained in one area are applied to another. However, collecting region-specific annotations for each area is costly or infeasible. To address this, we propose a self-distillation curriculum learning framework that generalizes pretrained localization models to unseen new areas. Our approach introduces a Dirichlet-based quality assessment strategy to evaluate teacher-generated pseudo labels, where high uncertainty signals noisy predictions and low uncertainty indicates clean samples. This uncertainty is used to guide an easy-to-hard curriculum learning strategy, where easy samples are prioritized initially, and more challenging samples are progressively incorporated, enabling effective student training. Furthermore, we develop a joint optimization scheme that updates both the student model and pseudo labels, applying adaptive label smoothing to mitigate label noises and taking full advantage of new area data. Extensive experimental results on the VIGOR and KITTI benchmarks demonstrate that our method outperforms state-of-the-art approaches in new area localization, achieving superior accuracy without additional supervision.